code: def create_log(self, x, y, out, batch_idx, **kwargs):
      log = super().create_log(x, y, out, batch_idx, **kwargs)
      if self.log_interval > 0:
          log["interpretation"] = self._log_interpretation(out)
      return log
  
imports: from copy import copy
  from typing import Dict, List, Tuple, Union
  from matplotlib import pyplot
  import numpy
  import torch
  from torch import nn
  from torchmetrics import Metric
  from pytorch_forecasting.data import TimeSeriesDataSet
  from pytorch_forecasting.data.encoders import NaNLabelEncoder
  from pytorch_forecasting.metrics import MAE, MAPE, MASE, RMSE, SMAPE, MultiHorizonMetric, MultiLoss, QuantileLoss
  from pytorch_forecasting.models.base_model import BaseModelWithCovariates
  from pytorch_forecasting.models.nn import LSTM, MultiEmbedding
  from pytorch_forecasting.models.temporal_fusion_transformer.sub_modules import AddNorm, GateAddNorm, GatedLinearUnit, GatedResidualNetwork, InterpretableMultiHeadAttention, VariableSelectionNetwork
  from pytorch_forecasting.utils import create_mask, detach, integer_histogram, masked_op, padded_stack, to_list
label_desc: Use image node_id 7 for calling the TemporalFusionTransformer obj's underlying member method code with example usage: obj.create_log(x, y, out, batch_idx) and returns: log